RNA_Content_Analysis_SPARQ_12122016
nathanbrandt
2016-12-21
Experiment overview
Write a detailed description of your experiment here including the goal of the analysis and your interpretation of the results.
If you still see this text it means that you have not described the experiment and whatever follows is meaningless. ###############################
This code is designed for use with the Accuri flow cytometer, which is equiped with the following lasers and filters
- Blue laser (488 nm)
- FL1 filter = 514/20nm GFP
FL3 filter = 575/25nm YFP
- Yellow/green laser (552 nm)
- FL2 filter = 610/20nm mCherry, dtomato
FL4 filter = 586/15nm DsRed
Requirements
In order to run this code you need:
+ to predefine your gates using the gating.R script
+ the gates.Rdata workspace, which contains the gates used in this script
+ the path of the directory(ies), given the variable names dir1, dir2… that contain .fcs files named A01.fcs, A02.fcs, A03.fcs…
+ a tab delimited sample sheet in each directory with the following rows:
+ the variable names are changed in chunk 2 named “Variable Names”
Output
This script generates a summary of results followed by quality control plots.
1 Step 1: Load relevant libraries
# This is a function that just makes sure you have a package, or installs it for you without prompting
requireInstall <- function(packageName,isBioconductor=F) {
if ( !try(require(packageName,character.only=T)) ) {
print(paste0("You don't have ",packageName," accessible, ",
"I'm gonna install it"))
if (isBioconductor) {
source("http://bioconductor.org/biocLite.R")
biocLite(packageName)
} else {
install.packages("packageName", repos = "http://cran.us.r-project.org")
}
}
return(1)
}
#Load libraries
requireInstall("flowCore",isBioconductor=T)## Loading required package: flowCore## [1] 1requireInstall("flowViz",isBioconductor=T)## Loading required package: flowViz## Loading required package: lattice## [1] 1requireInstall("flowStats")## Loading required package: flowStats## Loading required package: fda## Loading required package: splines## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:flowCore':
##
## %&%##
## Attaching package: 'fda'## The following object is masked from 'package:graphics':
##
## matplot## Loading required package: cluster## Loading required package: flowWorkspace## Loading required package: ncdfFlow## Loading required package: RcppArmadillo## Loading required package: BH## Loading required package: gridExtra## [1] 1requireInstall("Hmisc")## Loading required package: Hmisc## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units## [1] 1requireInstall("reshape2")## Loading required package: reshape2## [1] 1requireInstall("ggplot2")## [1] 1requireInstall("flowWorkspace")## [1] 1requireInstall("ggcyto", isBioconductor=T)## Loading required package: ggcyto## [1] 1requireInstall("gridExtra")## [1] 12 Step 2: Read in .fcs files, an Rdata file containing the gates sample sheet(s) that contains four columns with
- column1 = Well
- column2 = Strain
- column3 = Staining
- column4 = Media
- column5 = Userdefined
#Read in all data for analysis. Data should be in individual directories that contain .fcs files and a corresponding sample sheet with a generic format. FCS file names should be unaltered e.g AO1.fcs, A02.fcs, ...H12.fcs
#An abitrary number of directories can be used named dir1, dir2, dir3...with a corresponding flowData.1, flowData.2, flowData.3...and sample.sheet.1, sample.sheet.2, sample.sheet.3...
#load the Rdata file containing the gates
#load("gates.Rdata")
#Define the directory, or directories, containing your .fcs files using absolute path names
dir1 <- "/Users/nathanbrandt/Google Drive/Transfer_Folder/RNA_Content_Analysis_SPARQ/TotalRNA_FY4_Varied_Media_12122016"
#Read in all the fcs files in the directory, with alter.names changing "-" to "."
flowData <- read.flowSet(path = dir1, pattern=".fcs", alter.names=TRUE)
#Read in the sample sheet that should be in each directory that contains the .fcs files.
sample.sheet <- read.csv("/Users/nathanbrandt/Google Drive/Transfer_Folder/RNA_Content_Analysis_SPARQ/TotalRNA_FY4_Varied_Media_SampleSheet_12122016.csv")
#Change names of samples to those specified in the sample sheets
sampleNames(flowData) <- paste(sample.sheet[,5], sample.sheet[,1], sep=" ")#Check how many cells were counted in each fcs file
fsApply(flowData, each_col, length)[1:6]## [1] 10000 10000 10000 10000 10000 10000total <- fsApply(flowData, each_col, length)[1:6] #total counts per sample
#Print the medians of data values for each measurement
fsApply(flowData, each_col, median)## FSC.A SSC.A FL1.A FL2.A FL3.A FL4.A FSC.H
## YPD A01 700422.5 103008.0 16250.0 820.0 3294.0 709.5 1175290.0
## YPGal A02 727462.5 113155.5 21468.0 1060.0 4187.0 887.5 1153467.5
## YPGlyc A03 733878.5 120307.5 33087.0 1513.0 6075.0 1277.0 1088408.0
## GlnLim A04 729008.5 102508.5 78778.5 9861.5 20652.0 8282.5 1104204.5
## ProLim A05 584064.0 78498.5 134057.0 40452.0 84877.0 32658.0 905607.0
## SD A06 631467.5 86844.5 569561.5 25383.5 108294.5 20114.5 1003269.0
## YPD A07 668055.5 99630.0 16342.0 820.0 3280.0 697.0 1150440.0
## YPGal A08 645465.5 101051.5 19295.5 960.0 3856.0 807.5 1085250.5
## YPGlyc A09 685008.0 111254.0 34713.0 1550.0 6307.5 1297.0 1061765.0
## GlnLim A10 714631.5 101260.0 89843.5 7729.5 20676.0 6980.5 1083373.5
## ProLim A11 508300.5 68274.5 93553.0 14414.5 33308.5 11790.5 818136.0
## SD A12 669543.0 96052.5 47098.0 3024.0 10590.0 2541.0 1021929.5
## YPD B01 689990.5 103298.5 14933.5 764.0 3032.0 653.0 1202645.0
## YPGal B02 636482.5 102701.0 19119.0 954.0 3819.0 795.0 1076020.5
## YPGlyc B03 743083.0 113811.5 29266.5 1371.0 5400.5 1154.0 1103063.5
## GlnLim B04 716647.5 99960.0 78673.5 8259.0 19253.5 7782.5 1086203.5
## ProLim B05 545838.5 71152.0 129582.5 37403.0 78009.5 30654.0 864628.5
## SD B06 632818.5 89111.0 43396.0 2889.0 9586.5 2455.5 985152.0
## YPD B07 690964.0 103287.5 15927.5 783.0 3133.0 684.0 1160342.5
## YPGal B08 612823.5 97969.0 18622.5 953.5 3728.0 798.5 1046458.0
## YPGlyc B09 697293.0 108480.0 31868.0 1461.0 5894.0 1242.0 1069537.0
## GlnLim B10 685283.0 94043.5 93312.5 9431.5 22902.5 9087.0 1037134.5
## ProLim B11 501178.5 67249.5 148940.5 34201.5 79007.5 27609.5 815435.5
## SD B12 644792.5 91665.5 50930.5 3399.0 11361.0 2877.0 989039.5
## YPD C01 588045.5 96182.0 2241.0 263.0 628.0 269.0 1016572.0
## YPGal C02 574802.5 91550.5 1885.0 275.5 710.0 246.0 1018102.5
## YPGlyc C03 589825.5 86320.5 2521.0 362.0 905.0 339.0 967961.0
## GlnLim C04 655348.5 103820.0 3101.5 276.0 717.0 262.0 1007457.0
## ProLim C05 545356.5 81539.0 1852.0 246.0 604.0 235.0 895314.0
## SD C06 510689.0 85283.0 1951.0 251.0 620.0 242.0 871899.5
## YPD C07 541967.5 88490.5 2279.0 265.0 662.0 255.0 999675.0
## YPGal C08 559506.0 94609.0 1878.0 282.0 704.0 255.0 1017736.0
## YPGlyc C09 611277.5 95069.5 2637.0 374.0 948.0 343.0 1000991.0
## GlnLim C10 664830.5 116025.5 3466.0 281.0 717.0 248.0 1006457.5
## ProLim C11 507901.5 79932.5 1706.0 229.0 524.0 210.0 857547.5
## SD C12 499223.5 80675.0 1911.0 259.0 631.0 251.0 838800.0
## YPD D01 642248.0 95127.5 15002.0 773.0 3063.0 668.0 1179705.5
## YPGal D02 660056.0 107761.5 19795.0 1009.0 3966.5 833.0 1124747.5
## YPGlyc D03 757114.5 119133.5 33138.5 1514.0 6043.5 1265.0 1125010.0
## GlnLim D04 693133.5 97846.0 75486.5 6621.5 17975.5 6288.0 1046395.0
## ProLim D05 530360.5 68093.0 104844.0 14993.0 44508.5 13075.5 855412.5
## SD D06 649334.5 92796.5 37286.0 2604.0 8862.0 2186.0 1000390.5
## YPD E01 572400.0 85093.5 14205.0 744.0 2945.0 629.5 1064448.5
## YPGal E02 611759.0 99964.5 17790.5 919.0 3601.0 756.0 1075781.5
## YPGlyc E03 708123.0 111894.0 31872.5 1476.5 5880.0 1242.0 1090208.5
## GlnLim E04 675794.5 93982.5 78053.0 7503.5 18908.0 7212.5 1037018.5
## ProLim E05 548721.0 73619.0 123578.0 43740.0 86991.5 36233.0 874953.5
## SD E06 614461.5 84968.5 50181.5 3487.0 11297.0 3048.0 956665.5
## YPD F01 505371.5 80105.0 1828.0 263.0 607.0 270.0 987559.5
## YPGal F02 524767.0 81818.5 1645.0 266.0 659.0 236.0 1013445.5
## YPGlyc F03 590692.0 89956.0 2659.0 386.0 1015.0 366.0 988898.5
## GlnLim F04 668329.5 116880.5 3317.0 281.0 739.0 259.0 1016472.5
## ProLim F05 540595.0 82760.5 1798.0 240.0 595.0 224.0 895739.5
## SD F06 534227.0 85245.0 1788.0 235.0 599.0 227.0 894500.0
## SSC.H FL1.H FL2.H FL3.H FL4.H Width Time
## YPD A01 158575.0 18352.5 749.0 3572.0 726.0 57 187
## YPGal A02 164092.5 22170.5 912.0 4208.5 841.0 60 211
## YPGlyc A03 161299.0 30604.0 1229.0 5523.0 1094.5 62 309
## GlnLim A04 137744.5 73875.0 7499.0 18589.5 5904.0 62 401
## ProLim A05 108808.0 132761.5 35481.5 83379.5 25968.0 58 531
## SD A06 123917.0 613289.5 26465.0 115135.5 20691.5 58 254
## YPD A07 157939.0 18649.5 761.0 3632.5 722.0 55 184
## YPGal A08 154833.0 21152.0 881.0 4062.0 803.0 56 196
## YPGlyc A09 154508.5 33053.5 1300.5 5850.5 1148.5 60 257
## GlnLim A10 137717.0 83385.0 6086.5 18495.5 5196.5 61 441
## ProLim A11 96542.0 95165.5 12455.0 33079.5 9838.0 55 416
## SD A12 130967.5 44521.5 2499.0 9449.5 2122.5 60 398
## YPD B01 164756.0 17206.5 701.0 3398.0 686.0 56 188
## YPGal B02 159264.5 21282.5 879.0 4060.0 791.0 56 194
## YPGlyc B03 153113.0 27313.0 1111.0 5002.0 1001.0 62 341
## GlnLim B04 134877.0 72936.0 6237.5 16994.0 5541.0 62 379
## ProLim B05 98265.5 131453.5 33039.0 76993.5 24134.5 57 471
## SD B06 122797.0 42594.0 2428.5 8858.5 2057.5 59 437
## YPD B07 159120.5 17867.5 706.0 3432.0 690.0 57 197
## YPGal B08 150263.0 20685.0 867.0 4027.0 797.0 55 203
## YPGlyc B09 150229.5 30749.5 1217.0 5485.0 1093.0 60 289
## GlnLim B10 124931.5 86110.5 7160.5 20548.0 6597.5 61 445
## ProLim B11 95332.5 156629.0 31965.0 80903.5 22994.5 55 342
## SD B12 124565.5 47835.0 2761.0 10120.0 2352.5 59 461
## YPD C01 148862.0 2782.0 179.0 1459.0 398.0 54 188
## YPGal C02 145149.0 2421.0 190.0 1475.5 389.0 53 196
## YPGlyc C03 125222.5 2918.0 241.0 1590.0 440.0 56 244
## GlnLim C04 141115.0 3327.5 181.0 1514.0 406.0 60 330
## ProLim C05 116236.5 2298.0 164.0 1409.0 381.0 55 318
## SD C06 128582.5 2363.0 164.0 1556.0 382.0 54 225
## YPD C07 145583.5 2885.0 181.0 1477.0 392.0 52 179
## YPGal C08 151461.5 2435.0 193.0 1472.5 384.0 52 188
## YPGlyc C09 137578.0 2998.0 249.0 1603.0 442.0 57 234
## GlnLim C10 156418.0 3606.0 182.0 1554.0 399.0 61 330
## ProLim C11 115952.0 2154.5 153.0 1399.5 364.0 53 298
## SD C12 117483.5 2321.0 170.0 1442.0 390.0 54 254
## YPD D01 157539.5 18322.5 749.5 3565.0 706.0 53 184
## YPGal D02 169879.0 21848.0 920.0 4298.0 822.0 56 214
## YPGlyc D03 159091.5 31318.0 1247.0 5586.0 1095.0 63 346
## GlnLim D04 130988.0 70539.5 5159.5 15741.5 4612.0 61 549
## ProLim D05 97028.0 106283.5 13883.0 44824.0 11258.0 56 446
## SD D06 126248.5 35944.0 2193.0 8176.0 1848.0 59 535
## YPD E01 145474.5 17498.0 715.0 3463.0 676.0 52 190
## YPGal E02 162629.0 20337.0 853.0 4020.5 768.0 54 202
## YPGlyc E03 154542.0 30968.0 1259.0 5592.5 1097.0 61 305
## GlnLim E04 129158.5 74021.5 5886.5 17190.0 5322.0 60 495
## ProLim E05 103946.0 126383.0 38860.0 87012.5 28262.0 56 386
## SD E06 116649.0 47510.0 2850.0 10264.0 2463.5 58 419
## YPD F01 139479.5 2444.0 178.0 1380.0 385.0 49 181
## YPGal F02 139258.0 2203.0 185.0 1429.0 371.0 50 192
## YPGlyc F03 137580.0 3089.0 264.0 1646.0 458.0 56 245
## GlnLim F04 159359.0 3486.0 181.0 1524.0 405.0 60 323
## ProLim F05 120838.5 2240.0 160.0 1423.5 375.0 55 294
## SD F06 125843.0 2218.0 154.0 1431.0 379.0 55 262samples.num <- length(flowData) #number of samples3 Step 3: apply filters to data and generate plots showing the effect on filtering
##Subset the data by applying sequential gates##
#apply doublet gate
flowData.singlets <- Subset(flowData, pg.singlets)
fsApply(flowData.singlets, each_col, length)[1:samples.num]
singlets <- fsApply(flowData.singlets, each_col, length)[1:samples.num]
barplot(singlets/total, ylim=c(0,1), ylab = "Proportion singlet cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#apply debris gate
filteredData <- Subset(flowData.singlets, pg.nondebris)
fsApply(filteredData, each_col, length)[1:samples.num]
non.debris <- fsApply(filteredData, each_col, length)[1:samples.num]
barplot(non.debris/total, ylim=c(0,1), ylab = "Proportion singlet and nondebris cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#########
#filteredData is the variable name for the data filtered of doublets and debris that are used for all subsequent analyses
##########
#this gate defines nongfp cells
gfp.neg <- Subset(filteredData, pg.nongfp)
fsApply(gfp.neg, each_col, length)[1:samples.num]
non.gfp <- fsApply(gfp.neg, each_col, length)[1:samples.num]
barplot(non.gfp/non.debris, ylim=c(0,1), ylab = "Proportion cells with no GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines gfp cells
gfp.pos <- Subset(filteredData, pg.gfp)
fsApply(gfp.pos, each_col, length)[1:samples.num]
gfp.cells <- fsApply(gfp.pos, each_col, length)[1:samples.num]
barplot(gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines high GFP cells
gfp.hi <- Subset(filteredData, pg.hi.gfp)
fsApply(gfp.hi, each_col, length)[1:samples.num]
hi.gfp.cells <- fsApply(gfp.hi, each_col, length)[1:samples.num]
barplot(hi.gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with high GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))filteredData <- flowData4 Step 4: Data analysis
4.1 diagnostic values can be defined for plotting purposes
#define critical values that can superimposed on plots for easy visual comparison
gfp.bg <- 3.9 #a background value for GFP
gfp.wt <- 5.9 #a value for wildtype GFP expression
red.bg <- 3.03 #a background value for the red channel
red.wt <- 3.75 #a value for wildtype Red expression
haploid.fsc <- 6e5 #an empirical value for forward scatter for haploids
diploid.fsc <- 7e5 #an empirical value for forward scatter for diploids
gfp.norm <- 0.935 #an empricial value for gfp expression normalized by forward scatter
red.norm <- 0.57 #an empricial value for red expression normalized by forward scatter
gfp.red.norm <- 1.5 #an empricial value for gfp expression normalized by red channel4.2 Extract data from fcs files to generate statistics and boxplots
#record summary statistics for each sample in a matrix named summary.stats
summary.stats <- matrix(data = NA, nrow = length(filteredData), ncol = 18, dimnames = list(sampleNames(filteredData),c("FSC_median","FSC_mean", "FSC_sd","FL1_median", "FL1_mean","FL1_sd","normalizedGFP_median", "normalizedGFP_mean", "normalizedGFP_sd","FL2_median","FL2_mean","FL2_sd","normalizedRed_median","normalizedRed_mean", "normalizedRed_sd","GFPnormalizedByRed_median", "GFPnormalizedByRed_mean","GFPnormalizedByRed_sd")))
#use the sample containing the minimum number of points after filtering for doublets and debris to define the number of data points retained for all samples
sample.size <- min(fsApply(filteredData, each_col, length))
print(sample.size)## [1] 10000comparison.FSC <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
#for each sample plot a histogram of the normalized data, raw FSC and raw GFP per row
par(mfrow=c(1,2), mar=c(5.1,2.1,2.1,2.1), oma=c(1.5,2,1,1))
#extract data from flowFrames to plot histograms of values and record summary statistics
for (i in 1:length(filteredData)){
temp <- exprs(filteredData[[i]]) #exprs() extracts a matrix of the values from the flowframe
##########################################
#record summary statistics for the sample#
##########################################
#FSC
summary.stats[i,1] <- median(temp[,1])
summary.stats[i,2] <-mean(temp[,1])
summary.stats[i,3] <- sd(temp[,1])
#FL1
summary.stats[i,4] <- median(temp[,3])
summary.stats[i,5] <-mean(temp[,3])
summary.stats[i,6] <- sd(temp[,3])
#FL1 (GFP) divided by FSC
summary.stats[i,7] <- median(temp[,3]/temp[,1])
summary.stats[i,8] <-mean(temp[,3]/temp[,1])
summary.stats[i,9] <- sd(temp[,3]/temp[,1])
#FL2
summary.stats[i,10] <- median(temp[,4])
summary.stats[i,11] <-mean(temp[,4])
summary.stats[i,12] <- sd(temp[,4])
#FL2 (Red) divided by FSC
summary.stats[i,13] <- median(temp[,4]/temp[,1])
summary.stats[i,14] <-mean(temp[,4]/temp[,1])
summary.stats[i,15] <- sd(temp[,4]/temp[,1])
#FL1 (GFP) divided by FL2 (Red)
summary.stats[i,16] <- median(temp[,3]/temp[,4])
summary.stats[i,17] <-mean(temp[,3]/temp[,4])
summary.stats[i,18] <- sd(temp[,3]/temp[,4])
##############################################
#plot histograms of the channels of interest##
##############################################
###############
#Green channel#
###############
#FL1 (GFP)
hist(log10(temp[,3]), br=1000, xlab = "log10(FL1)", main = "FL1")
abline(v=gfp.bg, col="yellow", lty=2, lwd=2)
abline(v=gfp.wt, col="green", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 = ",round(median(temp[,3]), digits=4),sep=""))
#GFP divided by FSC
hist(temp[,3]/temp[,1], br=500, xlab = "FL1/FSC", main = "FL1/FSC")
abline(v=gfp.norm, col="green", lty=2, lwd=2 )
legend("topleft", legend=paste("median GFP / FSC=",round(median(temp[,3]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Red channel#
###############
#FL2 (Red)
hist(log10(temp[,4]), br=500, xlab = "log10(FL2)", main = "FL2")
abline(v=red.bg, col="yellow", lty=2, lwd=2)
abline(v=red.wt, col="red", lty=2, lwd=2)
legend("topleft", legend=paste("median FL2=",round(median(temp[,4]), digits=4),sep=""))
#FL2 divided by FSC
hist(temp[,4]/temp[,1], br=500, xlab = "FL2/FSC", main = "FL2/FSC")
abline(v=red.norm, col="red", lty=2, lwd=2 )
legend("topleft", legend=paste("median FL2 / FSC=",round(median(temp[,4]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Other#########
###############
#FL1 divided by FL2
hist(temp[,4]/temp[,3], br=500, xlab = "FL2/FL1", main = "FL1/FL2")
abline(v=gfp.red.norm, col="purple", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 / FL2=",round(median(temp[,4]/temp[,3]), digits=4),sep=""))
#FSC
hist(log10(temp[,1]), br=500, xlab = "log10(FSC)", main = "FSC", xlim=c(4,8))
abline(v=haploid.fsc, col="blue", lty=2, lwd=2)
abline(v=diploid.fsc, col="grey", lty=2, lwd=2)
legend("topleft", legend=paste("median FSC=",round(median(temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
print("-------------------------------------------------------")
print("-----------------------------------")
print("----------------------")
############################################################
#keep the data set for generating boxplots comparing values#
############################################################
#Note that the amount of data kept for each sample is defined by the lowest count among all the samples.
comparison.FSC[1:sample.size,i] <- temp[1:sample.size,1] #FSC
comparison.FL1[1:sample.size,i] <- temp[1:sample.size,3] #FL1 (GFP)
comparison.FL1NormFsc[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,1] #GFP/FSC
comparison.FL2[1:sample.size,i] <- temp[1:sample.size,4] #FL2
comparison.FL2NormFsc[1:sample.size,i] <- temp[1:sample.size,4]/temp[1:sample.size,1] #FL2/FSC
comparison.FL1NormFL2[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,4] #FL1/FL2
}
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## [1] "----------------------"par(mfrow=c(1,1)) #change number of plots per row back to standard4.3 Overview of data distributions
par(mar=c(8.1,4.1,4.1,2.1)) #create more space at lower margin
boxplot(comparison.FSC, names=sampleNames(filteredData), notch = TRUE, col = "gray", ylab="FSC", cex.axis=0.5,las=2, outline=F)
abline(h=haploid.fsc, lty=2, col=2)
abline(h=diploid.fsc, lty=2, col=3)
boxplot(comparison.FL1, names=sampleNames(filteredData), notch = TRUE, col = "lightgreen", ylab="FL1", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.bg ,lty=2, lwd=3, col="yellow")
abline(h=gfp.wt, lty = 2, lwd=3, col="green")
boxplot(comparison.FL1NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "green", ylab="FL1/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.norm, lty=2, lwd=3, col="blue")
boxplot(comparison.FL2, names=sampleNames(filteredData), notch = TRUE, col = "pink", ylab="FL2", cex.axis=0.5,las=2, outline=F)
abline(h=red.bg, lty=2, lwd=3, col="pink")
abline(h=red.wt, lty=2, lwd=3, col="red")
boxplot(comparison.FL2NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "red", ylab="FL2/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=red.norm, lty=2, lwd=3, col="red")
boxplot(comparison.FL1NormFL2, names=sampleNames(filteredData), notch = TRUE, col = "purple", ylab="FL1/FL2", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.red.norm, lty=2, lwd=3, col="purple")
par(mar=c(5.1,4.1,4.1,2.1)) #reset margins to default
#generate a summary table containing all the recorded statistics
print(summary.stats)## FSC_median FSC_mean FSC_sd FL1_median FL1_mean FL1_sd
## YPD A01 700422.5 727998.3 267753.6 16250.0 20833.523 55401.640
## YPGal A02 727462.5 765673.8 300662.0 21468.0 32208.636 87610.487
## YPGlyc A03 733878.5 822108.3 408704.7 33087.0 60060.102 172086.747
## GlnLim A04 729008.5 769335.7 286452.4 78778.5 387331.061 773137.168
## ProLim A05 584064.0 598978.4 268153.2 134057.0 460563.735 729574.374
## SD A06 631467.5 688839.7 312829.8 569561.5 901962.018 1110986.203
## YPD A07 668055.5 698972.6 258191.4 16342.0 23216.343 72952.862
## YPGal A08 645465.5 682737.7 292760.2 19295.5 28216.585 88094.002
## YPGlyc A09 685008.0 787898.5 431702.6 34713.0 66146.911 192104.489
## GlnLim A10 714631.5 755798.8 264333.2 89843.5 516829.818 918908.962
## ProLim A11 508300.5 530558.3 213500.3 93553.0 273831.631 423118.418
## SD A12 669543.0 729344.6 380279.7 47098.0 382496.913 1034546.372
## YPD B01 689990.5 717382.7 258329.7 14933.5 19824.937 56313.687
## YPGal B02 636482.5 679141.1 281693.1 19119.0 26219.482 65406.236
## YPGlyc B03 743083.0 813602.4 372118.4 29266.5 53506.039 160743.681
## GlnLim B04 716647.5 766842.1 298682.1 78673.5 486415.136 952642.413
## ProLim B05 545838.5 570964.6 249379.5 129582.5 328970.495 519221.426
## SD B06 632818.5 679180.2 404213.5 43396.0 283808.060 758313.186
## YPD B07 690964.0 724710.3 282369.1 15927.5 22934.595 73025.979
## YPGal B08 612823.5 656740.4 289987.7 18622.5 28951.995 86621.996
## YPGlyc B09 697293.0 787191.8 422260.5 31868.0 59436.625 193405.646
## GlnLim B10 685283.0 724053.0 273723.3 93312.5 620317.544 1045257.581
## ProLim B11 501178.5 533118.2 239965.4 148940.5 305968.770 426565.692
## SD B12 644792.5 698932.4 399688.4 50930.5 351003.928 792805.958
## YPD C01 588045.5 671507.7 314368.0 2241.0 2574.102 1958.569
## YPGal C02 574802.5 632386.5 285347.5 1885.0 2181.173 1726.457
## YPGlyc C03 589825.5 673316.3 335911.3 2521.0 2940.268 2288.271
## GlnLim C04 655348.5 701656.6 238402.5 3101.5 6323.994 56456.662
## ProLim C05 545356.5 595809.0 255398.3 1852.0 2076.636 2865.198
## SD C06 510689.0 589773.7 306327.6 1951.0 2276.439 1873.716
## YPD C07 541967.5 626076.0 309890.4 2279.0 2680.326 2151.240
## YPGal C08 559506.0 627285.3 307248.4 1878.0 2223.797 1856.830
## YPGlyc C09 611277.5 713960.7 401094.7 2637.0 3171.614 2607.655
## GlnLim C10 664830.5 723982.8 270887.7 3466.0 3710.098 2162.450
## ProLim C11 507901.5 545075.2 212989.1 1706.0 1872.425 1303.127
## SD C12 499223.5 594497.9 344124.8 1911.0 2311.145 2005.942
## YPD D01 642248.0 665196.4 237837.3 15002.0 21419.363 60484.552
## YPGal D02 660056.0 699236.1 289874.9 19795.0 29658.543 94007.495
## YPGlyc D03 757114.5 849509.4 411793.3 33138.5 57961.555 168666.743
## GlnLim D04 693133.5 722539.4 243194.3 75486.5 647630.602 1097889.347
## ProLim D05 530360.5 548926.4 230736.7 104844.0 342468.685 559822.965
## SD D06 649334.5 681435.6 314350.5 37286.0 210575.159 725211.701
## YPD E01 572400.0 591004.0 215354.1 14205.0 19554.618 62204.748
## YPGal E02 611759.0 652919.4 272376.4 17790.5 25295.294 77289.552
## YPGlyc E03 708123.0 794309.1 415165.6 31872.5 63823.007 208638.129
## GlnLim E04 675794.5 707687.9 245502.3 78053.0 587747.477 1037238.554
## ProLim E05 548721.0 581529.6 340389.6 123578.0 292671.161 476337.884
## SD E06 614461.5 702191.1 586539.4 50181.5 520708.512 1077170.930
## YPD F01 505371.5 560779.5 249817.5 1828.0 2093.101 1652.899
## YPGal F02 524767.0 564720.2 239534.9 1645.0 1920.735 2204.798
## YPGlyc F03 590692.0 698971.2 423900.6 2659.0 3628.924 4639.217
## GlnLim F04 668329.5 721083.9 258280.5 3317.0 4929.400 33084.550
## ProLim F05 540595.0 579926.7 233080.5 1798.0 2010.472 1596.587
## SD F06 534227.0 624898.9 360826.5 1788.0 2165.240 2000.262
## normalizedGFP_median normalizedGFP_mean normalizedGFP_sd
## YPD A01 0.023468570 0.031372660 0.102879588
## YPGal A02 0.029847480 0.046614192 0.149054152
## YPGlyc A03 0.043799274 0.103728646 0.383893776
## GlnLim A04 0.101478560 0.523562084 1.011966062
## ProLim A05 0.218522693 0.749249279 1.033593063
## SD A06 1.117647802 1.334210683 1.317366728
## YPD A07 0.024440223 0.036907672 0.150126988
## YPGal A08 0.031397046 0.047309515 0.169085146
## YPGlyc A09 0.049020536 0.121141493 0.441689196
## GlnLim A10 0.116659842 0.720435276 1.231159387
## ProLim A11 0.201215101 0.527282358 0.782627088
## SD A12 0.066742309 0.523340974 1.157975571
## YPD B01 0.021559560 0.029172426 0.106077112
## YPGal B02 0.030877536 0.042153246 0.133037402
## YPGlyc B03 0.038632967 0.092767708 0.360560963
## GlnLim B04 0.104161736 0.666977270 1.269269393
## ProLim B05 0.229473236 0.586972752 0.859135932
## SD B06 0.066458529 0.427345368 0.904033682
## YPD B07 0.022998644 0.033970332 0.129401176
## YPGal B08 0.031608798 0.052841084 0.182868143
## YPGlyc B09 0.045664003 0.111401865 0.382927370
## GlnLim B10 0.128827768 0.905403242 1.468935928
## ProLim B11 0.290477394 0.593573032 0.785631069
## SD B12 0.074770647 0.645819512 9.640024037
## YPD C01 0.003630415 0.003772173 0.002263945
## YPGal C02 0.003272437 0.003379200 0.002110278
## YPGlyc C03 0.004204772 0.004297259 0.002438130
## GlnLim C04 0.004534413 0.010989604 0.110453301
## ProLim C05 0.003331174 0.003551454 0.005610649
## SD C06 0.003668386 0.003831759 0.002191731
## YPD C07 0.004035893 0.004177105 0.002410326
## YPGal C08 0.003368830 0.003476163 0.002794658
## YPGlyc C09 0.004266000 0.004347393 0.002409461
## GlnLim C10 0.004995865 0.005196753 0.002809237
## ProLim C11 0.003311772 0.003501842 0.002276598
## SD C12 0.003714433 0.003837618 0.002277376
## YPD D01 0.023407320 0.035413306 0.124361273
## YPGal D02 0.030850996 0.048392839 0.179452375
## YPGlyc D03 0.043521612 0.085306085 0.356240517
## GlnLim D04 0.103949919 0.948179689 1.545281335
## ProLim D05 0.202412794 0.635482556 0.971553015
## SD D06 0.056016110 0.309100776 0.900306368
## YPD E01 0.024919104 0.036187514 0.141479693
## YPGal E02 0.029833788 0.043837382 0.159962059
## YPGlyc E03 0.044256660 0.116191186 0.422681156
## GlnLim E04 0.110661536 0.882006818 1.502094184
## ProLim E05 0.210782291 0.515154254 0.765328904
## SD E06 0.076107216 0.772071889 3.571464537
## YPD F01 0.003528091 0.003642102 0.002145753
## YPGal F02 0.003157455 0.003364310 0.003707234
## YPGlyc F03 0.004398656 0.005830850 0.009674120
## GlnLim F04 0.004767758 0.007781380 0.057888315
## ProLim F05 0.003318608 0.003520820 0.004038385
## SD F06 0.003255927 0.003402028 0.002438266
## FL2_median FL2_mean FL2_sd normalizedRed_median
## YPD A01 820.0 1031.3843 3272.5200 0.0011852294
## YPGal A02 1060.0 2061.6142 21397.9595 0.0014709326
## YPGlyc A03 1513.0 13744.7575 118241.9979 0.0019944377
## GlnLim A04 9861.5 35076.2459 79033.9734 0.0133261222
## ProLim A05 40452.0 66939.4855 165023.9246 0.0831028154
## SD A06 25383.5 41467.3279 81278.8609 0.0491696180
## YPD A07 820.0 1709.2766 20794.5900 0.0012325739
## YPGal A08 960.0 3700.5898 74954.1180 0.0015679164
## YPGlyc A09 1550.0 12853.6334 95827.6311 0.0021993298
## GlnLim A10 7729.5 31225.1817 59566.5975 0.0103002246
## ProLim A11 14414.5 41453.1937 75976.6839 0.0322492619
## SD A12 3024.0 25571.0544 122765.8548 0.0041688679
## YPD B01 764.0 1250.9815 20226.1060 0.0011205618
## YPGal B02 954.0 1515.7424 9958.8247 0.0015342487
## YPGlyc B03 1371.0 7654.3200 61507.7839 0.0018007329
## GlnLim B04 8259.0 41855.8518 102890.0000 0.0115028000
## ProLim B05 37403.0 52137.8978 67564.6672 0.0949772797
## SD B06 2889.0 22175.6039 132942.8002 0.0043034310
## YPD B07 783.0 1485.8256 15231.9458 0.0011464920
## YPGal B08 953.5 3673.3983 38090.4871 0.0015930250
## YPGlyc B09 1461.0 19583.4015 159713.4001 0.0020664748
## GlnLim B10 9431.5 45701.6683 80539.0325 0.0137032111
## ProLim B11 34201.5 46604.6846 80592.7087 0.0908027952
## SD B12 3399.0 28009.7950 162307.0857 0.0047877831
## YPD C01 263.0 289.1360 228.8965 0.0004204233
## YPGal C02 275.5 296.9361 228.8240 0.0004577977
## YPGlyc C03 362.0 397.0459 288.6906 0.0005835302
## GlnLim C04 276.0 641.5048 5231.8644 0.0004096650
## ProLim C05 246.0 272.0128 284.6269 0.0004359220
## SD C06 251.0 276.1797 218.8998 0.0004587432
## YPD C07 265.0 286.3186 224.6150 0.0004489657
## YPGal C08 282.0 307.7073 238.2803 0.0004772136
## YPGlyc C09 374.0 414.0593 306.8222 0.0005789695
## GlnLim C10 281.0 306.1860 241.5961 0.0004014518
## ProLim C11 229.0 253.0986 206.6592 0.0004378928
## SD C12 259.0 290.8147 235.3209 0.0004753731
## YPD D01 773.0 1622.3463 21170.6411 0.0012171228
## YPGal D02 1009.0 3747.6351 53449.6057 0.0015603125
## YPGlyc D03 1514.0 2905.0552 11475.6530 0.0019779240
## GlnLim D04 6621.5 41186.2797 75988.1403 0.0094750284
## ProLim D05 14993.0 42422.3672 108064.9237 0.0353480225
## SD D06 2604.0 19529.8930 76631.3821 0.0037871047
## YPD E01 744.0 1404.3030 17983.6277 0.0013188448
## YPGal E02 919.0 2754.8128 39023.3497 0.0015275482
## YPGlyc E03 1476.5 18125.1613 149690.8467 0.0020447089
## GlnLim E04 7503.5 41816.9987 81878.9129 0.0110125744
## ProLim E05 43740.0 63702.1915 121499.1757 0.1074041077
## SD E06 3487.0 38386.8420 219358.2760 0.0051169757
## YPD F01 263.0 279.7945 206.6551 0.0004947455
## YPGal F02 266.0 292.8793 384.4280 0.0004954633
## YPGlyc F03 386.0 486.7519 646.2197 0.0006083759
## GlnLim F04 281.0 664.4755 6977.0877 0.0004036873
## ProLim F05 240.0 268.1437 332.2801 0.0004365833
## SD F06 235.0 263.4086 224.5325 0.0003997738
## normalizedRed_mean normalizedRed_sd GFPnormalizedByRed_median
## YPD A01 0.0019079070 0.0228754388 NA
## YPGal A02 0.0069560319 0.1332456932 NA
## YPGlyc A03 0.0778199866 0.5474028523 NA
## GlnLim A04 0.0669439282 0.2685086586 NA
## ProLim A05 0.1617195268 0.5601718242 9.448369
## SD A06 0.0692898243 0.2170539902 NA
## YPD A07 0.0073195541 0.1578783244 NA
## YPGal A08 0.0189857009 0.2896493445 NA
## YPGlyc A09 0.0831629225 0.5594600329 NA
## GlnLim A10 0.0530554944 0.2054311360 16.065828
## ProLim A11 0.0985283220 0.3347124755 NA
## SD A12 0.0744173323 0.3954198793 NA
## YPD B01 0.0024515998 0.0754725790 NA
## YPGal B02 0.0059502374 0.1175966737 20.509651
## YPGlyc B03 0.0456871323 0.3978334236 NA
## GlnLim B04 0.0691963480 0.2848563945 12.498379
## ProLim B05 0.1050000457 0.2623621051 8.808692
## SD B06 0.0733881671 0.4975787722 NA
## YPD B07 0.0051005812 0.1124735098 20.892684
## YPGal B08 0.0255244540 0.3079508113 19.988286
## YPGlyc B09 0.1147535927 0.6566105797 NA
## GlnLim B10 0.0844734945 0.3090120339 NA
## ProLim B11 0.0914675609 0.1790336109 9.628653
## SD B12 0.1435845016 6.7173769161 NA
## YPD C01 0.0004692095 0.0003841369 NA
## YPGal C02 0.0005044239 0.0004080602 NA
## YPGlyc C03 0.0006306844 0.0006079741 NA
## GlnLim C04 0.0011620803 0.0103336308 NA
## ProLim C05 0.0005107088 0.0005921550 NA
## SD C06 0.0005127758 0.0004333698 NA
## YPD C07 0.0004962674 0.0003994202 NA
## YPGal C08 0.0005362125 0.0005553823 NA
## YPGlyc C09 0.0006206652 0.0004532229 NA
## GlnLim C10 0.0004562162 0.0004239614 NA
## ProLim C11 0.0005065730 0.0004446496 NA
## SD C12 0.0005397508 0.0004447254 NA
## YPD D01 0.0066946481 0.1507402399 19.970019
## YPGal D02 0.0191309187 0.2952946386 19.868315
## YPGlyc D03 0.0094514425 0.1265838302 NA
## GlnLim D04 0.0774226756 0.3018130088 NA
## ProLim D05 0.0862054227 0.1912583344 NA
## SD D06 0.0730919126 0.4514264269 NA
## YPD E01 0.0072939529 0.1724855718 19.612664
## YPGal E02 0.0140194345 0.2137475778 NA
## YPGlyc E03 0.1000718213 0.5750683091 21.922492
## GlnLim E04 0.0778888065 0.2764447570 NA
## ProLim E05 0.1268725473 0.3222995944 6.794650
## SD E06 0.0838451300 0.6342017201 NA
## YPD F01 0.0005373422 0.0004073990 NA
## YPGal F02 0.0005634731 0.0007137359 NA
## YPGlyc F03 0.0008748780 0.0016217317 NA
## GlnLim F04 0.0017337763 0.0527674296 NA
## ProLim F05 0.0005145308 0.0012124320 NA
## SD F06 0.0004674369 0.0004233192 NA
## GFPnormalizedByRed_mean GFPnormalizedByRed_sd
## YPD A01 NaN NA
## YPGal A02 NaN NA
## YPGlyc A03 NaN NA
## GlnLim A04 NaN NA
## ProLim A05 Inf NaN
## SD A06 NaN NA
## YPD A07 NaN NA
## YPGal A08 NaN NA
## YPGlyc A09 NaN NA
## GlnLim A10 16.16550 6.638098
## ProLim A11 NaN NA
## SD A12 NaN NA
## YPD B01 NaN NA
## YPGal B02 Inf NaN
## YPGlyc B03 NaN NA
## GlnLim B04 Inf NaN
## ProLim B05 9.53839 7.123417
## SD B06 NaN NA
## YPD B07 Inf NaN
## YPGal B08 Inf NaN
## YPGlyc B09 NaN NA
## GlnLim B10 NaN NA
## ProLim B11 10.17467 7.089692
## SD B12 NaN NA
## YPD C01 NaN NA
## YPGal C02 NaN NA
## YPGlyc C03 NaN NA
## GlnLim C04 NaN NA
## ProLim C05 NaN NA
## SD C06 NaN NA
## YPD C07 NaN NA
## YPGal C08 NaN NA
## YPGlyc C09 NaN NA
## GlnLim C10 NaN NA
## ProLim C11 NaN NA
## SD C12 NaN NA
## YPD D01 Inf NaN
## YPGal D02 Inf NaN
## YPGlyc D03 NaN NA
## GlnLim D04 NaN NA
## ProLim D05 NaN NA
## SD D06 NaN NA
## YPD E01 Inf NaN
## YPGal E02 NaN NA
## YPGlyc E03 Inf NaN
## GlnLim E04 NaN NA
## ProLim E05 Inf NaN
## SD E06 NaN NA
## YPD F01 NaN NA
## YPGal F02 NaN NA
## YPGlyc F03 NaN NA
## GlnLim F04 NaN NA
## ProLim F05 NaN NA
## SD F06 NaN NAsummary.stats <- as.data.frame(summary.stats)4.4 Quantitation of relative FL1 signal
baseline.FL1 <- summary.stats$FL1_median[1]
barplot(summary.stats$FL1_median/baseline.FL1, ylab="Relative FL1 median expression", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.5 Quantitation of forward scatter
baseline.FSC <- summary.stats$FSC_median[1]
barplot(summary.stats$FSC_median/baseline.FSC, ylab="Relative median FSC", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.6 Population composition
pop.composition <- rbind(non.gfp/non.debris,gfp.cells/non.debris,hi.gfp.cells/non.debris)
barplot(pop.composition, ylab="Proportion of population", legend=c("No GFP", "Normal GFP", "High GFP"),las=2, cex.names = 0.5,names.arg=sampleNames(filteredData))5 Step 5: Quality control
5.1 Gates
###First flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.1, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.1, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.1, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - high GFP gate")
################
###Second flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.2, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.2, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.2, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - high GFP gate")
#####Attempted as loop below to plot each one individually and is not working
for (i in 1:length(filteredData)){
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData[i], xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData[i], displayFilter=TRUE, xlim=c(0,3e5), ylim=c(0,3e6), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData[i], xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
}5.2 Data transformation for visualization
#In order to look at QC plots the data is transformed using the logicle transform, which is a log transform for high values that transitions to a linear transformation near zero values
#This is simply for visualization purposes
lgcl <- logicleTransform(w = 0.5, t= 10000, m=4.5) #the parameters w,t, and m define the transformation parameters
#Dataset 1 tranformation applied to every channel except width and time
dataLGCLTransform <- transform(filteredData,'FSC.A' = lgcl(`FSC.A`), 'SSC.A' =lgcl(`SSC.A`), 'FL1.A' = lgcl(`FL1.A`), 'FL2.A' = lgcl(`FL2.A`), 'FL3.A' = lgcl(`FL3.A`), 'FL4.A' = lgcl(`FL4.A`),'FSC.H' = lgcl(`FSC.H`),'SSC.H' = lgcl(`SSC.H`),'FL1.H' = lgcl(`FL1.H`),'FL2.H' = lgcl(`FL2.H`),'FL3.H' = lgcl(`FL3.H`),'FL4.H' = lgcl(`FL4.H`)) 5.3 Effect of time
#The effect of time on signal (of which there shouldn't be any)
i <- 1
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 2
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 3
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
}5.4 Plots of FL1 versus FSC
i <- 1
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(2,6), sampleNames(filteredData)[i])
}5.5 Plots of FSC versus SSC
i <- 1
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])
}